Magnetic resonance imaging (“MRI”) is a technique that finds wide and diverse use in clinical medicine. Clinical applications of MRI generally utilize contrast-weighted MR images, which are complex functions of intrinsic tissue MR parameters, such as spin density, longitudinal and transverse relaxation times, and extrinsic imperfections arising from pulse sequences and hardware. These images are qualitative in nature, and often have limited sensitivity to tissue physiological and/or pathological variations. This hampers the effectiveness of applying MRI for early disease diagnosis, such as cancer.
Although the potential of quantitative imaging has long been recognized, pushing towards truly quantitative imaging faces a number of technical challenges. One key challenge is long acquisition time. In order to obtain accurate quantitative tissue properties, a long sequence of images with different contrast-weightings has to be acquired. This often results in prohibitively long acquisition times, especially for high-resolution imaging applications.
Until recently, MR imaging speed has been significantly improved by the development of rapid scan sequences and parallel imaging with phased array coils. This dramatically increases the number of measurements possible in clinically relevant scan times, including multiple measurements at different relaxation weightings. Forming relaxation parameter maps from these images moves MR imaging experiments from the qualitative domain to the quantitative domain.
Magnetic resonance fingerprinting (“MRF”) is an imaging technique that enables quantitative mapping of tissue or other material properties based on random or pseudorandom measurements of the subject or object being imaged. In this context, “random” or “pseudorandom” measurements are achieved by performing an MRF pulse sequence formed by a series of “sequence blocks” that have differing acquisitions parameters between “sequence blocks,” such as differing flip angles, repetition times, echo times, and the like, as explained below. Using these variable sequence blocks, MRF enables simultaneous acquisition of multiple tissue-specific parameters and system-specific parameters at an ultrafast speed. Examples of tissue parameters that can be mapped include longitudinal relaxation time (T1), transverse relaxation time (T2), and spin or proton density (ρ), as well as experiment-specific parameters, such as off-resonance frequency. Advantageously, MRF provides a way to evaluate such parameters in a single, efficient imaging process. MRF is generally described in U.S. Pat. No. 8,723,518, which is herein incorporated by reference in its entirety.
Conventional MRF techniques vary the acquisition parameters from one repetition time (“TR”) period to the next, often in a random or pseudo-random way which creates a time series of images with varying contrast. Examples of acquisition parameters that can be varied include flip angle, radio frequency (“RF”) pulse phase, TR, echo time (“TE”), and k-space trajectories, such as by modifying one or more readout encoding gradients. The success of conventional MRF is largely due to the randomization of these parameters, which leads to both spatial and temporal incoherence of the encoding scheme but any scheme which creates a unique temporal pattern for different combinations of tissue parameters allows estimation of these parameters through the pattern matching process. More specifically, a sequence of randomized flip angles and repetition times (i.e., {(αm,TRm)}m=1M) is used to generate a sequence of images ({Im(x)}m=1M) with randomly varied contrast weightings, yielding incoherence in the temporal domain. Moreover, a set of highly undersampled k-space trajectories such as undersampled variable density spiral trajectories can be used to acquire k-space data, which yields spatial incoherence.
With these incoherently-sampled data, the conventional MRF reconstruction employs a simple template-matching procedure. Given a range of parameters of interest, the procedure uses a “dictionary” that contains all possible signal (or magnetization) evolutions simulated from the Bloch equation. That is, MRF matches an acquired magnetization signal to a pre-computed dictionary of signal models, or templates, that have been generated using Bloch equation-based physics simulations (i.e., Bloch simulations). As a general example, a template signal evolution is chosen from the dictionary if it yields the maximum correlation with the observed signal for each voxel (extracted from the gridding reconstructions). The parameters for the tissue or other material in a given voxel are estimated to be the values that provide the best signal template matching. That is, the reconstructed parameters are assigned as those that generate the selected template.
Although MRF has demonstrated great potential for revolutionizing MR parameter mapping, it does suffer from a number of key limitations. First, the accuracy of the resulting parameter maps depends critically on the length of data acquisition. Second, the accuracy of T2 maps is often much worse than that for T1 maps, especially when the acquisition time is short. Third, it is difficult for MRF to achieve high spatial resolution due to the limited SNR. The above observations clearly suggest that improving the SNR efficiency is of great importance to the design of MRF experiments.
In light of the foregoing, a need continues for systems and methods that advance the clinical ability to move toward quantitative imaging information, over qualitative imaging information.